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🌀 Twisting AI's Brain

Get ready for some mind-bending stuff! We're creating AI neural networks inspired by quantum mechanics. It's where physics meets technology meets ethics.

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QTC Qubit-Tensor-Chain

Algorithms: Hardware Agnostic AI

Formats: Error-Proof AI Components

Models: Chiral Neural Networks

Fusion: Hybrid Quantum-Classical Integration

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Finance: AI-Powered Civil Economies

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<aside> <img src="/icons/list_lightgray.svg" alt="/icons/list_lightgray.svg" width="40px" /> Contents

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<aside> <img src="/icons/skip-forward_blue.svg" alt="/icons/skip-forward_blue.svg" width="40px" /> Backlog:

• Initiation of 4th Path.
• Direct Development of Initial Code
• Documentation
• Refactoring
• Realignment
• Evaluation and Testing </aside>

Introduction

In the dynamic intersection of classical and quantum computing, our Qubit-Tensor-Chain (QTC) model offers a groundbreaking approach that mimics the sophisticated balance mechanisms found in quantum systems. This model, structured through scalar, vector, and matrix transformations, aims to provide a robust, error-correcting, and inherently balanced AI framework. By leveraging the YCrCb color space, the QTC model seamlessly integrates quantum principles into classical computation, enhancing the AI's ability to process and interpret complex data.

The YCrCb color space was chosen for our Qubit-Tensor-Chain (QTC) model due to its effective separation of luminance and chrominance components, which aligns well with our goal of simulating quantum computing principles in classical hardware. YCrCb divides an image into the Y component (luminance), capturing the brightness or intensity, and the Cr and Cb components (chrominance), representing color information. This separation allows us to independently manipulate and analyze the intensity and color variations, facilitating a nuanced and multi-dimensional data processing approach. By leveraging YCrCb, we enhance the model's ability to perform real-time error correction and balanced decision-making, essential for robust AI operations.

Steps and Key Components

Step 1. Qubit-Tensor-Chain Quantification Foundations - Scalar Polarity (Y):

• Primary Intensity Encoding: Utilizes the Y component (luminance) to encode the base intensity of data, aligning with how visual systems prioritize information.
• Vertical Flip for Balance: Simulates a Qubit Roll by flipping the tensor horizontally, which, in the context of images, is akin to a vertical flip. This helps in defining balance between the top and bottom of the image.
• Central Balance Point: Establishes a foundational balance by defining optimal energy height through scalar tensors, ensuring stability and a reference point for further data processing.

Step 2. Qubit-Tensor-Chain Quantitation Features - Vector Polarity (YCr):